# Transformers.js.py 🤗
[](https://github.com/whitphx/transformers.js.py/actions/workflows/main.yml)
[](https://pypi.org/project/transformers-js-py/)
**Use [Transformers.js](https://huggingface.co/docs/transformers.js/index) on [Pyodide](https://pyodide.org/en/stable/) and Pyodide-based frameworks such as [JupyterLite](https://jupyterlite.readthedocs.io/en/latest/), [stlite (Streamlit)](https://github.com/whitphx/stlite), [Shinylive (Shiny for Python)](https://shiny.posit.co/py/docs/shinylive.html), [PyScript](https://pyscript.net/), [HoloViz Panel](https://panel.holoviz.org), and so on.**
The original [Transformers](https://huggingface.co/docs/transformers/index) can't be used in a browser environment. [Transformers.js](https://huggingface.co/docs/transformers.js/index) is a JavaScript version of Transformers that can be installed on browsers, but we can't use it from Pyodide.
This package is **a thin wrapper of Transformers.js to proxy its API to Pyodide**.
## API
The API is more like Transformers.js than the original Transformers.
<table>
<tr>
<th width="50%" align="center"><b>Transformers.js</b></th>
<th width="50%" align="center"><b>Transformers.js.py</b></th>
</tr>
<tr>
<td>
```javascript
import { pipeline } from '@xenova/transformers';
// Allocate a pipeline for sentiment-analysis
let pipe = await pipeline('sentiment-analysis');
let out = await pipe('I love transformers!');
// [{'label': 'POSITIVE', 'score': 0.999817686}]
```
</td>
<td>
```python
from transformers_js_py import import_transformers_js
transformers = await import_transformers_js()
pipeline = transformers.pipeline
# Allocate a pipeline for sentiment-analysis
pipe = await pipeline('sentiment-analysis')
out = await pipe('I love transformers!')
# [{'label': 'POSITIVE', 'score': 0.999817686}]
```
</td>
</tr>
</table>
See the [Transformers.js document](https://github.com/xenova/transformers.js/) for available features.
### Special Case: `as_url()`
Certain methods of Transformers.js accept a URL as an input. However, when using Transformers.js.py on Pyodide, there may be instances where we want to pass a local file path from the virtual file system, rather than a URL. In such scenarios, the `as_url()` function can be used to convert a local file path into a URL.
```python
# Example
from transformers_js_py import import_transformers_js, as_url
transformers = await import_transformers_js()
pipeline = transformers.pipeline
pipe = await pipeline('image-classification')
local_image_path = "/path/to/image.jpg"
input_url = as_url(local_image_path) # Converts a local file path into a URL that can be passed to `pipe()`
result = await pipe(input_url)
```
## Examples
### JupyterLite
👉Try this code snippet on https://jupyter.org/try-jupyter/lab/index.html
```python
%pip install transformers_js_py
from transformers_js_py import import_transformers_js
transformers = await import_transformers_js()
pipeline = transformers.pipeline
pipe = await pipeline('sentiment-analysis')
out = await pipe('I love transformers!')
print(out)
```
### stlite (Serverless Streamlit)
[](https://edit.share.stlite.net/#!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,)
[👉 Online Demo](https://edit.share.stlite.net/#!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,) : try out this code online.
```python
import streamlit as st
from transformers_js_py import import_transformers_js
st.title("Sentiment analysis")
text = st.text_input("Input some text", "I love transformers!")
if text:
with st.spinner():
transformers = await import_transformers_js()
pipeline = transformers.pipeline
if "pipe" not in st.session_state:
st.session_state["pipe"] = await pipeline('sentiment-analysis')
pipe = st.session_state["pipe"]
out = await pipe(text)
st.write(out)
```
### Gradio-lite
Save the following code as an HTML file and open it in a browser.
```html
<!DOCTYPE html>
<html lang="en">
<head>
<script type="module" crossorigin src="https://cdn.jsdelivr.net/npm/@gradio/lite/dist/lite.js"></script>
<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/@gradio/lite/dist/lite.css" />
<title>Transformers.js with Gradio-lite</title>
<style>
html, body {
margin: 0;
padding: 0;
height: 100%;
width: 100%;
}
gradio-lite {
height: 100%;
width: 100%;
}
</style>
</head>
<body>
<gradio-lite>
<gradio-requirements>
transformers_js_py
</gradio-requirements>
<gradio-file name="app.py" entrypoint>
import gradio as gr
from transformers_js_py import import_transformers_js
transformers = await import_transformers_js()
pipeline = transformers.pipeline
pipe = await pipeline('sentiment-analysis')
async def process(text):
return await pipe(text)
demo = gr.Interface(fn=process, inputs="text", outputs="json")
demo.launch()
</gradio-file>
</gradio-lite>
</body>
</html>
```
[👉 Online demo](https://huggingface.co/spaces/abidlabs/gradio-lite-classify)
For more details about Gradio-lite, please read [Gradio-Lite: Serverless Gradio Running Entirely in Your Browser (huggingface.co)](https://huggingface.co/blog/gradio-lite).
### Shinylive
[](https://shinylive.io/py/editor/#code=NobwRAdghgtgpmAXGKAHVA6VBPMAaMAYwHsIAXOcpMAMwCdiYACAZwAsBLCbJjmVYnTJMAgujxM6lACZw6EgK4cAOhHqMmZOlAgsag+HRYB9AFYte-QcL4ChxrTr0G5J86tVpUxpUwC8TEpYUADmcMY0ADZK0gAUqkyJgRwYXKgKZA5wAB5k8WAUucr4TMUAKjk2EOlkxRKokVCEcGzEkbJ0fsUAouRympXFAJR4CUlBxBk1WbnGAG5yAEZQZHz5ZEVgI6pDHhCqsjSscgt0sWkZEpNkNRIscCwsHKRDiGOJAALXNe9MH1IQDoYQq1CBJJhQFjYCCEJiHTS5WKvX7gjhHCDEKo1YGVJFvMHgwmJKRkBR0MHFSn7AlExy6fR0QwWAJQADuUA4Nis9jpzkZrjMLCRKKSqA4qDgkS4cH8mm09JcRiw4sl0r2RMSYolsrZHOEWtVEDg+Xu5D4lDIAFodFBItgnixhuqNdcdezOUwDedqhkcYihrtqRrJHBSeTWFpYtdA3svLKxKhYl4fBw7ic5BJZIsFCE-GU6Ao4IGwABfPDgaDwahSACOSik8HILGBuXwRFIFCoyF5DKZlvMlpwpYAukA)
[👉 Online demo](https://shinylive.io/py/editor/#code=NobwRAdghgtgpmAXGKAHVA6VBPMAaMAYwHsIAXOcpMAMwCdiYACAZwAsBLCbJjmVYnTJMAgujxM6lACZw6EgK4cAOhHqMmZOlAgsag+HRYB9AFYte-QcL4ChxrTr0G5J86tVpUxpUwC8TEpYUADmcMY0ADZK0gAUqkyJgRwYXKgKZA5wAB5k8WAUucr4TMUAKjk2EOlkxRKokVCEcGzEkbJ0fsUAouRympXFAJR4CUlBxBk1WbnGAG5yAEZQZHz5ZEVgI6pDHhCqsjSscgt0sWkZEpNkNRIscCwsHKRDiGOJAALXNe9MH1IQDoYQq1CBJJhQFjYCCEJiHTS5WKvX7gjhHCDEKo1YGVJFvMHgwmJKRkBR0MHFSn7AlExy6fR0QwWAJQADuUA4Nis9jpzkZrjMLCRKKSqA4qDgkS4cH8mm09JcRiw4sl0r2RMSYolsrZHOEWtVEDg+Xu5D4lDIAFodFBItgnixhuqNdcdezOUwDedqhkcYihrtqRrJHBSeTWFpYtdA3svLKxKhYl4fBw7ic5BJZIsFCE-GU6Ao4IGwABfPDgaDwahSACOSik8HILGBuXwRFIFCoyF5DKZlvMlpwpYAukA) : try out this code online.
```python
from shiny import App, render, ui
from transformers_js_py import import_transformers_js
app_ui = ui.page_fluid(
ui.input_text("text", "Text input", placeholder="Enter text"),
ui.output_text_verbatim("txt"),
)
def server(input, output, session):
@output
@render.text
async def txt():
if not input.text():
return ""
transformers = await import_transformers_js()
pipeline = transformers.pipeline
pipe = await pipeline('sentiment-analysis')
out = await pipe(input.text())
return str(out)
app = App(app_ui, server, debug=True)
```
### PyScript
👉Try this code snippet on https://pyscript.com/
```html
<html>
<head>
<link rel="stylesheet" href="https://pyscript.net/latest/pyscript.css" />
<script defer src="https://pyscript.net/latest/pyscript.js"></script>
</head>
<body>
<input type="text" value="" id="text-input" />
<button py-click="run()" id="run-button">Run</button>
<py-config>
packages = ["transformers-js-py"]
</py-config>
<py-script>
import asyncio
from transformers_js_py import import_transformers_js
text_input = Element("text-input")
async def main(input_data):
transformers = await import_transformers_js()
pipeline = transformers.pipeline
pipe = await pipeline('sentiment-analysis')
out = await pipe(input_data)
print(out)
def run():
print("Start")
input_data = text_input.value
if input_data.strip() == "":
print("No data input.")
return
future = asyncio.ensure_future(main(input_data))
</py-script>
</body>
</html>
```
### Panel
With [HoloViz Panel](https://panel.holoviz.org) you develop your app on your laptop and convert it to [Pyodide](https://pyodide.org/en/stable/) or [PyScript](https://pyscript.net/) by running [`panel convert`](https://panel.holoviz.org/how_to/wasm/convert.html).
Install the requirements
```bash
pip install panel transformers_js_py
```
Create the **app.py** file in your favorite editor or IDE.
```python
import panel as pn
pn.extension(sizing_mode="stretch_width", design="material")
@pn.cache
async def _get_pipeline(model="sentiment-analysis"):
from transformers_js_py import import_transformers_js
transformers = await import_transformers_js()
return await transformers.pipeline(model)
text_input = pn.widgets.TextInput(placeholder="Send a message", name="Message")
button = pn.widgets.Button(name="Send", icon="send", align="end", button_type="primary")
@pn.depends(text_input, button)
async def _response(text, event):
if not text:
return {}
pipe = await _get_pipeline()
return await pipe(text)
pn.Column(
text_input, button, pn.pane.JSON(_response, depth=2)
).servable()
```
Convert the app to [Pyodide](https://pyodide.org/en/stable/). For more options like *hot reload* check out the [Panel Convert](https://panel.holoviz.org/how_to/wasm/convert.html) guide.
```bash
panel convert app.py --to pyodide-worker --out pyodide --requirements transformers_js_py
```
Now serve the app
```bash
python -m http.server -d pyodide
```
Finally you can try out the app by opening [localhost:8000/app.html](http://localhost:8000/app.html)
<details>
<summary><h4>Panel Chat App Example</h4></summary>
You can also use `transformers_js_py` with [Panels Chat Components](https://panel.holoviz.org/reference/index.html#chat).
```python
import panel as pn
MODEL = "sentiment-analysis"
pn.chat.ChatMessage.default_avatars["hugging face"] = "🤗"
pn.extension(design="material")
@pn.cache
async def _get_pipeline(model):
from transformers_js_py import import_transformers_js
transformers = await import_transformers_js()
return await transformers.pipeline(model)
async def callback(contents: str, user: str, instance: pn.chat.ChatInterface):
pipe = await _get_pipeline(MODEL)
response = await pipe(contents)
label, score = response[0]["label"], round(response[0]["score"], 2)
return f"""I feel a {label} vibe here (score: {score})"""
welcome_message = pn.chat.ChatMessage(
f"I'm a Hugging Face Transformers `{MODEL}` model.\n\nPlease *send a message*!",
user="Hugging Face",
)
pn.chat.ChatInterface(
welcome_message, placeholder_text="Loading the model ...",
callback=callback, callback_user="Hugging Face",
).servable()
```
For more chat examples see [Panel Chat Examples](https://holoviz-topics.github.io/panel-chat-examples/).
</details>
Raw data
{
"_id": null,
"home_page": "https://github.com/whitphx/transformers.js.py",
"name": "transformers-js-py",
"maintainer": null,
"docs_url": null,
"requires_python": "<4.0,>=3.10",
"maintainer_email": null,
"keywords": null,
"author": "Yuichiro Tachibana (Tsuchiya)",
"author_email": "t.yic.yt@gmail.com",
"download_url": "https://files.pythonhosted.org/packages/4a/1f/a4780e1113be6f2e9c8a0407cdb8f41353cb0f0b20a0e1400f1ad9bd8494/transformers_js_py-0.18.1.tar.gz",
"platform": null,
"description": "# Transformers.js.py \ud83e\udd17\n\n[](https://github.com/whitphx/transformers.js.py/actions/workflows/main.yml)\n[](https://pypi.org/project/transformers-js-py/)\n\n**Use [Transformers.js](https://huggingface.co/docs/transformers.js/index) on [Pyodide](https://pyodide.org/en/stable/) and Pyodide-based frameworks such as [JupyterLite](https://jupyterlite.readthedocs.io/en/latest/), [stlite (Streamlit)](https://github.com/whitphx/stlite), [Shinylive (Shiny for Python)](https://shiny.posit.co/py/docs/shinylive.html), [PyScript](https://pyscript.net/), [HoloViz Panel](https://panel.holoviz.org), and so on.**\n\nThe original [Transformers](https://huggingface.co/docs/transformers/index) can't be used in a browser environment. [Transformers.js](https://huggingface.co/docs/transformers.js/index) is a JavaScript version of Transformers that can be installed on browsers, but we can't use it from Pyodide.\nThis package is **a thin wrapper of Transformers.js to proxy its API to Pyodide**.\n\n## API\n\nThe API is more like Transformers.js than the original Transformers.\n\n<table>\n<tr>\n<th width=\"50%\" align=\"center\"><b>Transformers.js</b></th>\n<th width=\"50%\" align=\"center\"><b>Transformers.js.py</b></th>\n</tr>\n<tr>\n<td>\n\n```javascript\nimport { pipeline } from '@xenova/transformers';\n\n// Allocate a pipeline for sentiment-analysis\nlet pipe = await pipeline('sentiment-analysis');\n\nlet out = await pipe('I love transformers!');\n// [{'label': 'POSITIVE', 'score': 0.999817686}]\n```\n\n</td>\n<td>\n\n```python\nfrom transformers_js_py import import_transformers_js\n\ntransformers = await import_transformers_js()\npipeline = transformers.pipeline\n\n# Allocate a pipeline for sentiment-analysis\npipe = await pipeline('sentiment-analysis')\n\nout = await pipe('I love transformers!')\n# [{'label': 'POSITIVE', 'score': 0.999817686}]\n```\n\n</td>\n</tr>\n</table>\n\nSee the [Transformers.js document](https://github.com/xenova/transformers.js/) for available features.\n\n### Special Case: `as_url()`\nCertain methods of Transformers.js accept a URL as an input. However, when using Transformers.js.py on Pyodide, there may be instances where we want to pass a local file path from the virtual file system, rather than a URL. In such scenarios, the `as_url()` function can be used to convert a local file path into a URL.\n\n```python\n# Example\nfrom transformers_js_py import import_transformers_js, as_url\n\ntransformers = await import_transformers_js()\npipeline = transformers.pipeline\npipe = await pipeline('image-classification')\n\nlocal_image_path = \"/path/to/image.jpg\"\n\ninput_url = as_url(local_image_path) # Converts a local file path into a URL that can be passed to `pipe()`\nresult = await pipe(input_url)\n```\n\n## Examples\n\n### JupyterLite\n\n\n\n\ud83d\udc49Try this code snippet on https://jupyter.org/try-jupyter/lab/index.html\n\n```python\n%pip install transformers_js_py\n\nfrom transformers_js_py import import_transformers_js\n\ntransformers = await import_transformers_js()\npipeline = transformers.pipeline\n\npipe = await pipeline('sentiment-analysis')\n\nout = await pipe('I love transformers!')\n\nprint(out)\n```\n\n### stlite (Serverless Streamlit)\n\n[](https://edit.share.stlite.net/#!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,)\n\n[\ud83d\udc49 Online Demo](https://edit.share.stlite.net/#!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,) : try out this code online.\n\n```python\nimport streamlit as st\n\nfrom transformers_js_py import import_transformers_js\n\nst.title(\"Sentiment analysis\")\n\ntext = st.text_input(\"Input some text\", \"I love transformers!\")\n\nif text:\n with st.spinner():\n transformers = await import_transformers_js()\n pipeline = transformers.pipeline\n if \"pipe\" not in st.session_state:\n st.session_state[\"pipe\"] = await pipeline('sentiment-analysis')\n pipe = st.session_state[\"pipe\"]\n out = await pipe(text)\n st.write(out)\n```\n\n### Gradio-lite\n\n\n\nSave the following code as an HTML file and open it in a browser.\n\n```html\n<!DOCTYPE html>\n<html lang=\"en\">\n<head>\n <script type=\"module\" crossorigin src=\"https://cdn.jsdelivr.net/npm/@gradio/lite/dist/lite.js\"></script>\n <link rel=\"stylesheet\" href=\"https://cdn.jsdelivr.net/npm/@gradio/lite/dist/lite.css\" />\n <title>Transformers.js with Gradio-lite</title>\n <style>\n html, body {\n margin: 0;\n padding: 0;\n height: 100%;\n width: 100%;\n }\n gradio-lite {\n height: 100%;\n width: 100%;\n }\n </style>\n</head>\n<body>\n<gradio-lite>\n\n<gradio-requirements>\ntransformers_js_py\n</gradio-requirements>\n\n<gradio-file name=\"app.py\" entrypoint>\nimport gradio as gr\nfrom transformers_js_py import import_transformers_js\n\ntransformers = await import_transformers_js()\npipeline = transformers.pipeline\n\npipe = await pipeline('sentiment-analysis')\n\nasync def process(text):\n return await pipe(text)\n\ndemo = gr.Interface(fn=process, inputs=\"text\", outputs=\"json\")\n\ndemo.launch()\n</gradio-file>\n\n</gradio-lite>\n</body>\n</html>\n```\n\n[\ud83d\udc49 Online demo](https://huggingface.co/spaces/abidlabs/gradio-lite-classify)\n\nFor more details about Gradio-lite, please read [Gradio-Lite: Serverless Gradio Running Entirely in Your Browser (huggingface.co)](https://huggingface.co/blog/gradio-lite).\n\n### Shinylive\n\n[](https://shinylive.io/py/editor/#code=NobwRAdghgtgpmAXGKAHVA6VBPMAaMAYwHsIAXOcpMAMwCdiYACAZwAsBLCbJjmVYnTJMAgujxM6lACZw6EgK4cAOhHqMmZOlAgsag+HRYB9AFYte-QcL4ChxrTr0G5J86tVpUxpUwC8TEpYUADmcMY0ADZK0gAUqkyJgRwYXKgKZA5wAB5k8WAUucr4TMUAKjk2EOlkxRKokVCEcGzEkbJ0fsUAouRympXFAJR4CUlBxBk1WbnGAG5yAEZQZHz5ZEVgI6pDHhCqsjSscgt0sWkZEpNkNRIscCwsHKRDiGOJAALXNe9MH1IQDoYQq1CBJJhQFjYCCEJiHTS5WKvX7gjhHCDEKo1YGVJFvMHgwmJKRkBR0MHFSn7AlExy6fR0QwWAJQADuUA4Nis9jpzkZrjMLCRKKSqA4qDgkS4cH8mm09JcRiw4sl0r2RMSYolsrZHOEWtVEDg+Xu5D4lDIAFodFBItgnixhuqNdcdezOUwDedqhkcYihrtqRrJHBSeTWFpYtdA3svLKxKhYl4fBw7ic5BJZIsFCE-GU6Ao4IGwABfPDgaDwahSACOSik8HILGBuXwRFIFCoyF5DKZlvMlpwpYAukA)\n\n[\ud83d\udc49 Online demo](https://shinylive.io/py/editor/#code=NobwRAdghgtgpmAXGKAHVA6VBPMAaMAYwHsIAXOcpMAMwCdiYACAZwAsBLCbJjmVYnTJMAgujxM6lACZw6EgK4cAOhHqMmZOlAgsag+HRYB9AFYte-QcL4ChxrTr0G5J86tVpUxpUwC8TEpYUADmcMY0ADZK0gAUqkyJgRwYXKgKZA5wAB5k8WAUucr4TMUAKjk2EOlkxRKokVCEcGzEkbJ0fsUAouRympXFAJR4CUlBxBk1WbnGAG5yAEZQZHz5ZEVgI6pDHhCqsjSscgt0sWkZEpNkNRIscCwsHKRDiGOJAALXNe9MH1IQDoYQq1CBJJhQFjYCCEJiHTS5WKvX7gjhHCDEKo1YGVJFvMHgwmJKRkBR0MHFSn7AlExy6fR0QwWAJQADuUA4Nis9jpzkZrjMLCRKKSqA4qDgkS4cH8mm09JcRiw4sl0r2RMSYolsrZHOEWtVEDg+Xu5D4lDIAFodFBItgnixhuqNdcdezOUwDedqhkcYihrtqRrJHBSeTWFpYtdA3svLKxKhYl4fBw7ic5BJZIsFCE-GU6Ao4IGwABfPDgaDwahSACOSik8HILGBuXwRFIFCoyF5DKZlvMlpwpYAukA) : try out this code online.\n\n```python\nfrom shiny import App, render, ui\nfrom transformers_js_py import import_transformers_js\n\napp_ui = ui.page_fluid(\n ui.input_text(\"text\", \"Text input\", placeholder=\"Enter text\"),\n ui.output_text_verbatim(\"txt\"),\n)\n\n\ndef server(input, output, session):\n @output\n @render.text\n async def txt():\n if not input.text():\n return \"\"\n\n transformers = await import_transformers_js()\n pipeline = transformers.pipeline\n\n pipe = await pipeline('sentiment-analysis')\n\n out = await pipe(input.text())\n\n return str(out)\n\n\napp = App(app_ui, server, debug=True)\n```\n\n### PyScript\n\n\n\n\ud83d\udc49Try this code snippet on https://pyscript.com/\n\n```html\n<html>\n <head>\n <link rel=\"stylesheet\" href=\"https://pyscript.net/latest/pyscript.css\" />\n <script defer src=\"https://pyscript.net/latest/pyscript.js\"></script>\n </head>\n <body>\n <input type=\"text\" value=\"\" id=\"text-input\" />\n <button py-click=\"run()\" id=\"run-button\">Run</button>\n\n <py-config>\n packages = [\"transformers-js-py\"]\n </py-config>\n <py-script>\n import asyncio\n from transformers_js_py import import_transformers_js\n\n text_input = Element(\"text-input\")\n\n async def main(input_data):\n transformers = await import_transformers_js()\n pipeline = transformers.pipeline\n pipe = await pipeline('sentiment-analysis')\n out = await pipe(input_data)\n print(out)\n\n def run():\n print(\"Start\")\n input_data = text_input.value\n if input_data.strip() == \"\":\n print(\"No data input.\")\n return\n\n future = asyncio.ensure_future(main(input_data))\n </py-script>\n </body>\n</html>\n\n```\n\n### Panel\n\nWith [HoloViz Panel](https://panel.holoviz.org) you develop your app on your laptop and convert it to [Pyodide](https://pyodide.org/en/stable/) or [PyScript](https://pyscript.net/) by running [`panel convert`](https://panel.holoviz.org/how_to/wasm/convert.html).\n\n\n\nInstall the requirements\n\n```bash\npip install panel transformers_js_py\n```\n\nCreate the **app.py** file in your favorite editor or IDE.\n\n```python\nimport panel as pn\n\npn.extension(sizing_mode=\"stretch_width\", design=\"material\")\n\n@pn.cache\nasync def _get_pipeline(model=\"sentiment-analysis\"):\n from transformers_js_py import import_transformers_js\n transformers = await import_transformers_js()\n return await transformers.pipeline(model)\n\n\ntext_input = pn.widgets.TextInput(placeholder=\"Send a message\", name=\"Message\")\nbutton = pn.widgets.Button(name=\"Send\", icon=\"send\", align=\"end\", button_type=\"primary\")\n\n@pn.depends(text_input, button)\nasync def _response(text, event):\n if not text:\n return {}\n pipe = await _get_pipeline()\n return await pipe(text)\n\npn.Column(\n text_input, button, pn.pane.JSON(_response, depth=2)\n).servable()\n```\n\nConvert the app to [Pyodide](https://pyodide.org/en/stable/). For more options like *hot reload* check out the [Panel Convert](https://panel.holoviz.org/how_to/wasm/convert.html) guide.\n\n```bash\npanel convert app.py --to pyodide-worker --out pyodide --requirements transformers_js_py\n```\n\nNow serve the app\n\n```bash\npython -m http.server -d pyodide\n```\n\nFinally you can try out the app by opening [localhost:8000/app.html](http://localhost:8000/app.html)\n\n<details>\n <summary><h4>Panel Chat App Example</h4></summary>\n\nYou can also use `transformers_js_py` with [Panels Chat Components](https://panel.holoviz.org/reference/index.html#chat).\n\n\n\n```python\nimport panel as pn\n\nMODEL = \"sentiment-analysis\"\npn.chat.ChatMessage.default_avatars[\"hugging face\"] = \"\ud83e\udd17\"\n\npn.extension(design=\"material\")\n\n@pn.cache\nasync def _get_pipeline(model):\n from transformers_js_py import import_transformers_js\n transformers = await import_transformers_js()\n return await transformers.pipeline(model)\n\nasync def callback(contents: str, user: str, instance: pn.chat.ChatInterface):\n pipe = await _get_pipeline(MODEL)\n response = await pipe(contents)\n label, score = response[0][\"label\"], round(response[0][\"score\"], 2)\n return f\"\"\"I feel a {label} vibe here (score: {score})\"\"\"\n\nwelcome_message = pn.chat.ChatMessage(\n f\"I'm a Hugging Face Transformers `{MODEL}` model.\\n\\nPlease *send a message*!\",\n user=\"Hugging Face\",\n)\n\npn.chat.ChatInterface(\n welcome_message, placeholder_text=\"Loading the model ...\",\n callback=callback, callback_user=\"Hugging Face\",\n).servable()\n```\n\nFor more chat examples see [Panel Chat Examples](https://holoviz-topics.github.io/panel-chat-examples/).\n</details>\n",
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